report_bug
Report a bug, missing feature, or send feedback. Include the conversation array with recent messages for reproduction.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| context | No | ||
| message | Yes | ||
| conversation | No | [] |
Report a bug, missing feature, or send feedback. Include the conversation array with recent messages for reproduction.
| Name | Required | Description | Default |
|---|---|---|---|
| context | No | ||
| message | Yes | ||
| conversation | No | [] |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate idempotentHint=true and destructiveHint=false, so the description adds little about safety. The description mentions 'send feedback' but does not explain what happens after reporting (e.g., creates a ticket, sends an email) or any side effects. It also refers to a 'conversation array' while the schema specifies it as a string, creating a slight ambiguity. Overall, the description fails to add meaningful behavioral context beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences with zero filler. It front-loads the primary action and then gives a specific usage tip, earning every word. Structure is excellent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description should explain what happens after the report is submitted, but it does not. The parameter semantics are incomplete (context unexplained, conversation type mismatch). The tool's complexity is low, but the description misses critical return-value and parameter details, leaving the agent guessing about the outcome.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With schema description coverage at 0%, the description is solely responsible for explaining parameters. It only hints at 'conversation' by saying 'conversation array', but the schema defines it as a string (likely JSON-encoded). It does not explain 'context' at all, and 'message' is only implicitly understood as the main content. The description adds minimal value beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Report a bug, missing feature, or send feedback.' This is a specific verb+resource construction, and it distinguishes itself from the tiflux siblings by its general reporting nature. It could be more explicit about what makes it unique compared to tiflux_tickets_feedback_report, but the core purpose is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides one clear usage instruction: 'Include the conversation array with recent messages for reproduction.' This implies when to use the tool (for bug reports where reproduction is needed) but does not specify when not to use it or mention any alternatives. It lacks explicit exclusions or comparison to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose, from ticket lifecycle operations (create, cancel, close, update) to list/get actions for various entities (clients, desks, contracts, etc.). Even similar tools like create_ticket_answer vs create_internal_communication are well-differentiated in their descriptions, with explicit cross-references. The platform-level tools (authenticate, marketplace, etc.) are also unambiguous.
All tiflux-domain tools follow a consistent pattern: tiflux_ + verb (create, get, list, update, cancel, close) + noun (ticket, client, desk, appointment). Proper nouns like tiflux_tickets_feedback_report deviate slightly but still align with the tiflux_ prefix and use a clear descriptive name. Platform tools are uniformly simple verbs (authenticate, connect) or noun phrases (toolkit_info), maintaining internal consistency.
With 36 tools, the server is well above the 25-tool threshold that starts to feel overwhelming. While each tool appears to serve a distinct purpose within a complex ticketing system, the sheer volume could burden an agent with too many options, increasing selection difficulty and cognitive load. A more curated set (e.g., 15-20) might suffice for most workflows.
The tool surface covers the full ticket lifecycle (creation, listing, details, updates, cancellation, closure) plus supporting entities (clients, desks, stages, priorities, SLAs, knowledge base, requestors, technical groups, appointments, files, feedback reports). The inclusion of bulk operations and multi-id fetches indicates thorough consideration of agent needs. No critical gaps are apparent for the stated purpose of ticketing and helpdesk management.